To tackle these problems, researchers have used different ways to assess the trustworthiness of tweets and assign an overall rank to users [1]}. Castillo et al. [2]} measured the credibility of tweets (news topics) based on Twitter features. More precisely, an automated classification technique to detect news from conversational topics was used. Alex Hai Wang [3]} used followers and friends parameters to calculate the reputation score, which further aided user classification (i.e. to detect spammers).
Additionally, Saito and Masuda [4]} considered these metrics while assigning a rank to Twitter users.
In [5]}, the authors analysed tweets relevant to the Mumbai attackshttps://www.theguardian.com/world/blog/2011/jul/13/mumbai-blasts.
Their analysis showed that most information providers were unknown while the reputation of the others (based on number of followers) was very low.
In another study [6]} that looked at the same event, an information retrieval technique and machine learning algorithm found that only 17% of the tweets related to the underlying attacks were credible.
